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Tang H, Dai Z, Jiang Y, Li T, Liu C. PCG Classification Using Multidomain Features and SVM Classifier. BIOMED RESEARCH INTERNATIONAL 2018; 2018:4205027. [PMID: 30112388 PMCID: PMC6077676 DOI: 10.1155/2018/4205027] [Citation(s) in RCA: 20] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 03/20/2018] [Accepted: 06/12/2018] [Indexed: 11/26/2022]
Abstract
This paper proposes a method using multidomain features and support vector machine (SVM) for classifying normal and abnormal heart sound recordings. The database was provided by the PhysioNet/CinC Challenge 2016. A total of 515 features are extracted from nine feature domains, i.e., time interval, frequency spectrum of states, state amplitude, energy, frequency spectrum of records, cepstrum, cyclostationarity, high-order statistics, and entropy. Correlation analysis is conducted to quantify the feature discrimination abilities, and the results show that "frequency spectrum of state", "energy", and "entropy" are top domains to contribute effective features. A SVM with radial basis kernel function was trained for signal quality estimation and classification. The SVM classifier is independently trained and tested by many groups of top features. It shows the average of sensitivity, specificity, and overall score are high up to 0.88, 0.87, and 0.88, respectively, when top 400 features are used. This score is competitive to the best previous scores. The classifier has very good performance with even small number of top features for training and it has stable output regardless of randomly selected features for training. These simulations demonstrate that the proposed features and SVM classifier are jointly powerful for classifying heart sound recordings.
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Affiliation(s)
- Hong Tang
- Department of Biomedical Engineering, Dalian University of Technology, Dalian, China
| | - Ziyin Dai
- Department of Biomedical Engineering, Dalian University of Technology, Dalian, China
| | - Yuanlin Jiang
- Department of Biomedical Engineering, Dalian University of Technology, Dalian, China
| | - Ting Li
- College of Information and Communication Engineering, Dalian Minzu University, Dalian, China
| | - Chengyu Liu
- School of Instrument Science and Engineering, Southeast University, Nanjing, China
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Islam MA, Bandyopadhyaya I, Bhattacharyya P, Saha G. Multichannel lung sound analysis for asthma detection. COMPUTER METHODS AND PROGRAMS IN BIOMEDICINE 2018; 159:111-123. [PMID: 29650306 DOI: 10.1016/j.cmpb.2018.03.002] [Citation(s) in RCA: 28] [Impact Index Per Article: 4.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 08/07/2017] [Revised: 02/25/2018] [Accepted: 03/09/2018] [Indexed: 06/08/2023]
Abstract
BACKGROUND AND OBJECTIVE Lung sound signals convey valuable information of the lung status. Auscultation is an effective technique to appreciate the condition of the respiratory system using lung sound signals. The prior works on asthma detection from lung sound signals rely on the presence of wheeze. In this paper, we have classified normal and asthmatic subjects using advanced signal processing of posterior lung sound signals, even in the absence of wheeze. METHODS We collected lung sounds of 60 subjects (30 normal and 30 asthma) using a novel 4-channel data acquisition system from four different positions over the posterior chest, as suggested by the pulmonologist. A spectral subband based feature extraction scheme is proposed that works with artificial neural network (ANN) and support vector machine (SVM) classifiers for the multichannel signal. The power spectral density (PSD) is estimated from extracted lung sound cycle using Welch's method, which then decomposed into uniform subbands. A set of statistical features is computed from each subband and applied to ANN and SVM classifiers to classify normal and asthmatic subjects. RESULTS In the first part of this study, the performances of each individual channel and four channels together are evaluated where the combined channel performance is found superior to that of individual channels. Next, the performances of all possible combinations of the channels are investigated and the best classification accuracies of 89.2( ± 3.87)% and 93.3( ± 3.10)% are achieved for 2-channel and 3-channel combinations in ANN and SVM classifiers, respectively. CONCLUSIONS The proposed multichannel asthma detection method where the presence of wheeze in lung sound is not a necessary requirement, outperforms commonly used lung sound classification methods in this field and provides significant relative improvement. The channel combination study gives insight into the contribution of respective lung sound collection areas and their combinations in asthma detection.
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Affiliation(s)
- Md Ariful Islam
- Department of Electronics and Electrical Communication Engineering, Indian Institute of Technology, Kharagpur, Kharagpur 721302, India.
| | - Irin Bandyopadhyaya
- Department of Electronics and Electrical Communication Engineering, Indian Institute of Technology, Kharagpur, Kharagpur 721302, India.
| | | | - Goutam Saha
- Department of Electronics and Electrical Communication Engineering, Indian Institute of Technology, Kharagpur, Kharagpur 721302, India.
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Ibarra-Hernández RF, Alonso-Arévalo MA, Cruz-Gutiérrez A, Licona-Chávez AL, Villarreal-Reyes S. Design and evaluation of a parametric model for cardiac sounds. Comput Biol Med 2017; 89:170-180. [PMID: 28810184 DOI: 10.1016/j.compbiomed.2017.08.007] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/18/2017] [Revised: 07/25/2017] [Accepted: 08/03/2017] [Indexed: 11/17/2022]
Abstract
Heart sound analysis plays an important role in the auscultative diagnosis process to detect the presence of cardiovascular diseases. In this paper we propose a novel parametric heart sound model that accurately represents normal and pathological cardiac audio signals, also known as phonocardiograms (PCG). The proposed model considers that the PCG signal is formed by the sum of two parts: one of them is deterministic and the other one is stochastic. The first part contains most of the acoustic energy. This part is modeled by the Matching Pursuit (MP) algorithm, which performs an analysis-synthesis procedure to represent the PCG signal as a linear combination of elementary waveforms. The second part, also called residual, is obtained after subtracting the deterministic signal from the original heart sound recording and can be accurately represented as an autoregressive process using the Linear Predictive Coding (LPC) technique. We evaluate the proposed heart sound model by performing subjective and objective tests using signals corresponding to different pathological cardiac sounds. The results of the objective evaluation show an average Percentage of Root-Mean-Square Difference of approximately 5% between the original heart sound and the reconstructed signal. For the subjective test we conducted a formal methodology for perceptual evaluation of audio quality with the assistance of medical experts. Statistical results of the subjective evaluation show that our model provides a highly accurate approximation of real heart sound signals. We are not aware of any previous heart sound model rigorously evaluated as our proposal.
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Affiliation(s)
- Roilhi F Ibarra-Hernández
- Departamento de Electrónica y Telecomunicaciones, División de Física Aplicada, Centro de Investigación Científica y de Educación Superior de Ensenada, Carretera Ensenada-Tijuana No. 3918, CP 22860, Ensenada, B.C., Mexico.
| | - Miguel A Alonso-Arévalo
- Departamento de Electrónica y Telecomunicaciones, División de Física Aplicada, Centro de Investigación Científica y de Educación Superior de Ensenada, Carretera Ensenada-Tijuana No. 3918, CP 22860, Ensenada, B.C., Mexico.
| | - Alejandro Cruz-Gutiérrez
- Departamento de Electrónica y Telecomunicaciones, División de Física Aplicada, Centro de Investigación Científica y de Educación Superior de Ensenada, Carretera Ensenada-Tijuana No. 3918, CP 22860, Ensenada, B.C., Mexico.
| | - Ana L Licona-Chávez
- Facultad de Medicina, Centro de Estudios Universitarios Xochicalco Campus Ensenada, San Francisco 1139, Fraccionamiento Misión, CP 22830, Ensenada, B.C., Mexico.
| | - Salvador Villarreal-Reyes
- Departamento de Electrónica y Telecomunicaciones, División de Física Aplicada, Centro de Investigación Científica y de Educación Superior de Ensenada, Carretera Ensenada-Tijuana No. 3918, CP 22860, Ensenada, B.C., Mexico.
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Kadi I, Idri A, Fernandez-Aleman JL. Systematic mapping study of data mining–based empirical studies in cardiology. Health Informatics J 2017; 25:741-770. [DOI: 10.1177/1460458217717636] [Citation(s) in RCA: 10] [Impact Index Per Article: 1.4] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/16/2022]
Abstract
Data mining provides the methodology and technology to transform huge amount of data into useful information for decision making. It is a powerful process to extract knowledge and discover new patterns embedded in large data sets. Data mining has been increasingly used in medicine, particularly in cardiology. In fact, data mining applications can greatly benefits all parts involved in cardiology such as patients, cardiologists and nurses. This article aims to perform a systematic mapping study so as to analyze and synthesize empirical studies on the application of data mining techniques in cardiology. A total of 142 articles published between 2000 and 2015 were therefore selected, studied and analyzed according to the four following criteria: year and channel of publication, research type, medical task and empirical type. The results of this mapping study are discussed and a list of recommendations for researchers and cardiologists is provided.
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Affiliation(s)
| | - Ali Idri
- Mohammed V University in Rabat, Morocco
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Maknickas V, Maknickas A. Recognition of normal–abnormal phonocardiographic signals using deep convolutional neural networks and mel-frequency spectral coefficients. Physiol Meas 2017; 38:1671-1684. [DOI: 10.1088/1361-6579/aa7841] [Citation(s) in RCA: 50] [Impact Index Per Article: 7.1] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/12/2022]
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56
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Langley P, Murray A. Heart sound classification from unsegmented phonocardiograms. Physiol Meas 2017; 38:1658-1670. [DOI: 10.1088/1361-6579/aa724c] [Citation(s) in RCA: 40] [Impact Index Per Article: 5.7] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/11/2022]
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Plesinger F, Viscor I, Halamek J, Jurco J, Jurak P. Heart sounds analysis using probability assessment. Physiol Meas 2017; 38:1685-1700. [DOI: 10.1088/1361-6579/aa7620] [Citation(s) in RCA: 26] [Impact Index Per Article: 3.7] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/12/2022]
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Abdollahpur M, Ghaffari A, Ghiasi S, Mollakazemi MJ. Detection of pathological heart sounds. Physiol Meas 2017; 38:1616-1630. [PMID: 28594641 DOI: 10.1088/1361-6579/aa7840] [Citation(s) in RCA: 33] [Impact Index Per Article: 4.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/11/2022]
Abstract
Heart sound analysis has been a major topic of research over the past few decades. However, the necessity for a large and reliable database has been a major concern in these studies. OBJECTIVE Noting that the current heart sound classification methods do not work properly for noisy signals, the PhysioNet/CinC Challenge 2016 aims to develop the heart sound classification algorithms by providing a global open database for challengers. This paper addresses the problem of heart sound classification methods within noisy real-world phonocardiogram recordings by implementing an innovative approach. SIGNIFICANCE After locating the fundamental heart sounds and the systolic and diastolic components, a novel method named cycle quality assessment is applied to each recording. The presented method detects those cycles which are less affected by noise and better segmented by the use of two criteria here proposed in this paper. The selected cycles are the inputs of a further feature extraction process. APPROACH Due to the variability of the heart sound signal induced by various cardiac arrhythmias, four sets of features from the time, time-frequency and perceptual domains are extracted. Before starting the main classification process, the obtained 90-dimensional feature vector is mapped to a new feature space to pre-detect normal recordings by applying a Fisher's discriminant analysis. The main classification procedure is then done based on three feed-forward neural networks and a voting system among classifiers. MAIN RESULTS The presented method is evaluated using the training and hidden test sets of the PhysioNet/CinC Challenge 2016. Also, the results are compared with the top five ranked submissions. The results indicate that the proposed method is effective in classifying heart sounds as normal versus abnormal recordings.
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Affiliation(s)
- Mostafa Abdollahpur
- CardioVascular Research Group (CVRG), Department of Mechanical Engineering at K. N., Toosi University of Technology, Tehran, Iran
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Moghaddasi H, Almasganj F, Zoroufian A. Imaging of heart acoustic based on the sub-space methods using a microphone array. COMPUTER METHODS AND PROGRAMS IN BIOMEDICINE 2017; 146:133-142. [PMID: 28688483 DOI: 10.1016/j.cmpb.2017.04.017] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 02/14/2016] [Revised: 04/03/2017] [Accepted: 04/11/2017] [Indexed: 06/07/2023]
Abstract
BACKGROUND AND OBJECTIVES Heart disease is one of the leading causes of death around the world. Phonocardiogram (PCG) is an important bio-signal which represents the acoustic activity of heart, typically without any spatiotemporal information of the involved acoustic sources. The aim of this study is to analyze the PCG by employing a microphone array by which the heart internal sound sources could be localized, too. METHOD In this paper, it is intended to propose a modality by which the locations of the active sources in the heart could also be investigated, during a cardiac cycle. In this way, a microphone array with six microphones is employed as the recording set up to be put on the human chest. In the following, the Group Delay MUSIC algorithm which is a sub-space based localization method is used to estimate the location of the heart sources in different phases of the PCG. RESULTS We achieved to 0.14cm mean error for the sources of first heart sound (S1) simulator and 0.21cm mean error for the sources of second heart sound (S2) simulator with Group Delay MUSIC algorithm. The acoustical diagrams created for human subjects show distinct patterns in various phases of the cardiac cycles such as the first and second heart sounds. Moreover, the evaluated source locations for the heart valves are matched with the ones that are obtained via the 4-dimensional (4D) echocardiography applied, to a real human case. CONCLUSIONS Imaging of heart acoustic map presents a new outlook to indicate the acoustic properties of cardiovascular system and disorders of valves and thereby, in the future, could be used as a new diagnostic tool.
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Affiliation(s)
- Hanie Moghaddasi
- Biomedical Engineering Department, Amirkabir University of Technology, Tehran, Iran
| | - Farshad Almasganj
- Biomedical Engineering Department, Amirkabir University of Technology, Tehran, Iran.
| | - Arezoo Zoroufian
- Department of Cardiology, Tehran Heart Center, Tehran University of Medical Science, Tehran, Iran
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Puri C, Singh R, Bandyopadhyay S, Ukil A, Mukherjee A. Analysis of phonocardiogram signals through proactive denoising using novel self-discriminant learner. ANNUAL INTERNATIONAL CONFERENCE OF THE IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. ANNUAL INTERNATIONAL CONFERENCE 2017; 2017:2753-2756. [PMID: 29060468 DOI: 10.1109/embc.2017.8037427] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.1] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/07/2023]
Abstract
Phonocardiogram (PCG) records heart sound and murmurs, which contains significant information of cardiac health. Analysis of PCG signal has the potential to detect abnormal cardiac condition. However, the presence of noise and motion artifacts in PCG hinders the accuracy of clinical event detection. Thus, noise detection and elimination are crucial to ensure accurate clinical analysis. In this paper, we present a robust denoising technique, Proclean that precisely detects the noisy PCG signal through pattern recognition, and statistical learning. We propose a novel self-discriminant learner that ensures to obtain distinct feature set to distinguish clean and noisy PCG signals without human-in-loop. We demonstrate that our proposed denoising leads to higher accuracy in subsequent clinical analytics for medical investigation. Our extensive experimentations with publicly available MIT-Physionet datasets show that we achieve more than 85% accuracy for noisy PCG signal detection. Further, we establish that physiological abnormality detection improves by more than 20%, when our proposed denoising mechanism is applied.
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Arabasadi Z, Alizadehsani R, Roshanzamir M, Moosaei H, Yarifard AA. Computer aided decision making for heart disease detection using hybrid neural network-Genetic algorithm. COMPUTER METHODS AND PROGRAMS IN BIOMEDICINE 2017; 141:19-26. [PMID: 28241964 DOI: 10.1016/j.cmpb.2017.01.004] [Citation(s) in RCA: 139] [Impact Index Per Article: 19.9] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 09/11/2016] [Revised: 12/18/2016] [Accepted: 01/12/2017] [Indexed: 05/28/2023]
Abstract
Cardiovascular disease is one of the most rampant causes of death around the world and was deemed as a major illness in Middle and Old ages. Coronary artery disease, in particular, is a widespread cardiovascular malady entailing high mortality rates. Angiography is, more often than not, regarded as the best method for the diagnosis of coronary artery disease; on the other hand, it is associated with high costs and major side effects. Much research has, therefore, been conducted using machine learning and data mining so as to seek alternative modalities. Accordingly, we herein propose a highly accurate hybrid method for the diagnosis of coronary artery disease. As a matter of fact, the proposed method is able to increase the performance of neural network by approximately 10% through enhancing its initial weights using genetic algorithm which suggests better weights for neural network. Making use of such methodology, we achieved accuracy, sensitivity and specificity rates of 93.85%, 97% and 92% respectively, on Z-Alizadeh Sani dataset.
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Affiliation(s)
- Zeinab Arabasadi
- Department of Computer Engineering, University of Bojnord, Bojnord, Iran
| | - Roohallah Alizadehsani
- Department of Computer Engineering, Sharif University of Technology, Azadi Ave, Tehran, Iran.
| | - Mohamad Roshanzamir
- Department of Electrical and Computer Engineering, Isfahan University of Technology, Isfahan, Iran
| | - Hossein Moosaei
- Department of Mathematics, Faculty of Science, University of Bojnord, Iran
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Mobile Phonocardiogram Diagnosis in Newborns Using Support Vector Machine. Healthcare (Basel) 2017; 5:healthcare5010016. [PMID: 28335471 PMCID: PMC5371922 DOI: 10.3390/healthcare5010016] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/15/2016] [Revised: 02/12/2017] [Accepted: 03/15/2017] [Indexed: 12/20/2022] Open
Abstract
Phonocardiogram (PCG) monitoring on newborns is one of the most important and challenging tasks in the heart assessment in the early ages of life. In this paper, we present a novel approach for cardiac monitoring applied in PCG data. This basic system coupled with denoising, segmentation, cardiac cycle selection and classification of heart sound can be used widely for a large number of the data. This paper describes the problems and additional advantages of the PCG method including the possibility of recording heart sound at home, removing unwanted noises and data reduction on a mobile device, and an intelligent system to diagnose heart diseases on the cloud server. A wide range of physiological features from various analysis domains, including modeling, time/frequency domain analysis, an algorithm, etc., is proposed in order to extract features which will be considered as inputs for the classifier. In order to record the PCG data set from multiple subjects over one year, an electronic stethoscope was used for collecting data that was connected to a mobile device. In this study, we used different types of classifiers in order to distinguish between healthy and pathological heart sounds, and a comparison on the performances revealed that support vector machine (SVM) provides 92.2% accuracy and AUC = 0.98 in a time of 1.14 seconds for training, on a dataset of 116 samples.
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Karar ME, El-Khafif SH, El-Brawany MA. Automated Diagnosis of Heart Sounds Using Rule-Based Classification Tree. J Med Syst 2017; 41:60. [PMID: 28247307 DOI: 10.1007/s10916-017-0704-9] [Citation(s) in RCA: 20] [Impact Index Per Article: 2.9] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/29/2016] [Accepted: 02/09/2017] [Indexed: 10/20/2022]
Abstract
In order to assist the diagnosis procedure of heart sound signals, this paper presents a new automated method for classifying the heart status using a rule-based classification tree into normal and three abnormal cases; namely the aortic valve stenosis, aortic insufficient, and ventricular septum defect. The developed method includes three main steps as follows. First, one cycle of the heart sound signals is automatically detected and segmented based on time properties of the heart signals. Second, the segmented cycle is preprocessed with the discrete wavelet transform and then largest Lyapunov exponents are calculated to generate the dynamical features of heart sound time series. Finally, a rule-based classification tree is fed by these Lyapunov exponents to give the final decision of the heart health status. The developed method has been tested successfully on twenty-two datasets of normal heart sounds and murmurs with success rate of 95.5%. The resulting error can be easily corrected by modifying the classification rules; consequently, the accuracy of automated heart sounds diagnosis is further improved.
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Affiliation(s)
- Mohamed Esmail Karar
- Faculty of Electronic Engineering (FEE), Menoufia University, Menouf, 32952, Egypt.
| | - Sahar H El-Khafif
- Faculty of Electronic Engineering (FEE), Menoufia University, Menouf, 32952, Egypt
| | - Mohamed A El-Brawany
- Faculty of Electronic Engineering (FEE), Menoufia University, Menouf, 32952, Egypt
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64
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Zhang W, Han J, Deng S. Heart sound classification based on scaled spectrogram and partial least squares regression. Biomed Signal Process Control 2017. [DOI: 10.1016/j.bspc.2016.10.004] [Citation(s) in RCA: 68] [Impact Index Per Article: 9.7] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/24/2022]
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Kim J, Kim T, Lee D, Kim JW, Lee K. Exploiting temporal and nonstationary features in breathing sound analysis for multiple obstructive sleep apnea severity classification. Biomed Eng Online 2017; 16:6. [PMID: 28086902 PMCID: PMC5234114 DOI: 10.1186/s12938-016-0306-7] [Citation(s) in RCA: 10] [Impact Index Per Article: 1.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/17/2016] [Accepted: 12/21/2016] [Indexed: 11/22/2022] Open
Abstract
Background Polysomnography (PSG) is the gold standard test for obstructive sleep apnea (OSA), but it incurs high costs, requires inconvenient measurements, and is limited by a one-night test. Thus, a repetitive OSA screening test using affordable data would be effective both for patients interested in their own OSA risk and in-hospital PSG. The purpose of this research was to develop a four-OSA severity classification model using a patient’s breathing sounds. Methods Breathing sounds were recorded from 83 subjects during a PSG test. There was no exclusive experimental protocol or additional recording instruments use throughout the sound recording procedure. Based on the Apnea-Hypopnea Index (AHI), which indicates the severity of sleep apnea, the subjects’ sound data were divided into four-OSA severity classes. From the individual sound data, we proposed two novel methods which were not attempted in previous OSA severity classification studies. First, the total transition probability of approximated sound energy in time series, and second, the statistical properties derived from the dimension-reduced cyclic spectral density. In addition, feature selection was conducted to achieve better results with a more relevant subset of features. Then, the classification model was trained using support vector machines and evaluated using leave-one-out cross-validation. Results The overall results show that our classification model is better than existing multiple OSA severity classification method using breathing sounds. The proposed method demonstrated 79.52% accuracy for the four-class classification task. Additionally, it demonstrated 98.0% sensitivity, 75.0% specificity, and 92.78% accuracy for OSA subject detection classification with AHI threshold 5. Conclusions The results show that our proposed method can be used as part of an OSA screening test, which can provide the subject with detailed OSA severity results from only breathing sounds.
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Affiliation(s)
- Jaepil Kim
- Graduate School of Convergence, Science and Technology, Seoul National University, 1 Gwanak-ro, Seoul, 08826, Republic of Korea
| | - Taehoon Kim
- Graduate School of Convergence, Science and Technology, Seoul National University, 1 Gwanak-ro, Seoul, 08826, Republic of Korea
| | - Donmoon Lee
- Graduate School of Convergence, Science and Technology, Seoul National University, 1 Gwanak-ro, Seoul, 08826, Republic of Korea
| | - Jeong-Whun Kim
- Department of Otorhinolaryngology, Seoul National University Bundang Hospital, Gumi-ro, Seongnam, 13620, Republic of Korea.
| | - Kyogu Lee
- Graduate School of Convergence, Science and Technology, Seoul National University, 1 Gwanak-ro, Seoul, 08826, Republic of Korea.
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Kadi I, Idri A, Fernandez-Aleman J. Knowledge discovery in cardiology: A systematic literature review. Int J Med Inform 2017; 97:12-32. [DOI: 10.1016/j.ijmedinf.2016.09.005] [Citation(s) in RCA: 49] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/11/2016] [Revised: 09/01/2016] [Accepted: 09/11/2016] [Indexed: 11/24/2022]
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67
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Liu C, Springer D, Li Q, Moody B, Juan RA, Chorro FJ, Castells F, Roig JM, Silva I, Johnson AE, Syed Z, Schmidt SE, Papadaniil CD, Hadjileontiadis L, Naseri H, Moukadem A, Dieterlen A, Brandt C, Tang H, Samieinasab M, Samieinasab MR, Sameni R, Mark RG, Clifford GD. An open access database for the evaluation of heart sound algorithms. Physiol Meas 2016; 37:2181-2213. [PMID: 27869105 PMCID: PMC7199391 DOI: 10.1088/0967-3334/37/12/2181] [Citation(s) in RCA: 221] [Impact Index Per Article: 27.6] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/11/2022]
Abstract
In the past few decades, analysis of heart sound signals (i.e. the phonocardiogram or PCG), especially for automated heart sound segmentation and classification, has been widely studied and has been reported to have the potential value to detect pathology accurately in clinical applications. However, comparative analyses of algorithms in the literature have been hindered by the lack of high-quality, rigorously validated, and standardized open databases of heart sound recordings. This paper describes a public heart sound database, assembled for an international competition, the PhysioNet/Computing in Cardiology (CinC) Challenge 2016. The archive comprises nine different heart sound databases sourced from multiple research groups around the world. It includes 2435 heart sound recordings in total collected from 1297 healthy subjects and patients with a variety of conditions, including heart valve disease and coronary artery disease. The recordings were collected from a variety of clinical or nonclinical (such as in-home visits) environments and equipment. The length of recording varied from several seconds to several minutes. This article reports detailed information about the subjects/patients including demographics (number, age, gender), recordings (number, location, state and time length), associated synchronously recorded signals, sampling frequency and sensor type used. We also provide a brief summary of the commonly used heart sound segmentation and classification methods, including open source code provided concurrently for the Challenge. A description of the PhysioNet/CinC Challenge 2016, including the main aims, the training and test sets, the hand corrected annotations for different heart sound states, the scoring mechanism, and associated open source code are provided. In addition, several potential benefits from the public heart sound database are discussed.
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Affiliation(s)
- Chengyu Liu
- Department of Biomedical Informatics, Emory University, USA
| | - David Springer
- Institute of Biomedical Engineering, Department of Engineering Science, University of Oxford, UK
| | - Qiao Li
- Department of Biomedical Informatics, Emory University, USA
| | - Benjamin Moody
- Institute for Medical Engineering & Science, Massachusetts Institute of Technology, USA
| | - Ricardo Abad Juan
- Department of Biomedical Engineering, Georgia Institute of Technology, USA
- ITACA Institute, Universitat Politecnica de Valencia, Spain
| | - Francisco J Chorro
- Service of Cardiology, Valencia University Clinic Hospital, INCLIVA, Spain
| | | | | | - Ikaro Silva
- Institute for Medical Engineering & Science, Massachusetts Institute of Technology, USA
| | - Alistair E.W. Johnson
- Institute for Medical Engineering & Science, Massachusetts Institute of Technology, USA
| | - Zeeshan Syed
- Department of Biomedical Engineering, University of Michigan, Ann Arbor, MI, USA
| | - Samuel E. Schmidt
- Department of Health Science and Technology, Aalborg University, Denmark
| | - Chrysa D. Papadaniil
- Department of Electrical and Computer Engineering, Aristotle University of Thessaloniki, Greece
| | | | - Hosein Naseri
- Department of Mechanical Engineering, K. N. Toosi University of Technology, Iran
| | - Ali Moukadem
- MIPS Laboratory, University of Haute Alsace, France
| | | | | | - Hong Tang
- Faculty of Electronic and Electrical Engineering, Dalian University of Technology, China
| | - Maryam Samieinasab
- School of Electrical & Computer Engineering, Shiraz University, Shiraz, Iran
| | | | - Reza Sameni
- School of Electrical & Computer Engineering, Shiraz University, Shiraz, Iran
| | - Roger G. Mark
- Institute for Medical Engineering & Science, Massachusetts Institute of Technology, USA
| | - Gari D. Clifford
- Department of Biomedical Informatics, Emory University, USA
- Department of Biomedical Engineering, Georgia Institute of Technology, USA
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68
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Classifying syndromes in Chinese medicine using multi-label learning algorithm with relevant features for each label. Chin J Integr Med 2016; 22:867-871. [PMID: 27783322 DOI: 10.1007/s11655-016-2264-0] [Citation(s) in RCA: 10] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/12/2013] [Indexed: 10/20/2022]
Abstract
OBJECTIVE To develop an effective Chinese Medicine (CM) diagnostic model of coronary heart disease (CHD) and to confifirm the scientifific validity of CM theoretical basis from an algorithmic viewpoint. METHODS Four types of objective diagnostic data were collected from 835 CHD patients by using a self-developed CM inquiry scale for the diagnosis of heart problems, a tongue diagnosis instrument, a ZBOX-I pulse digital collection instrument, and the sound of an attending acquisition system. These diagnostic data was analyzed and a CM diagnostic model was established using a multi-label learning algorithm (REAL). RESULTS REAL was employed to establish a Xin (Heart) qi defificiency, Xin yang defificiency, Xin yin defificiency, blood stasis, and phlegm fifive-card CM diagnostic model, which had recognition rates of 80.32%, 89.77%, 84.93%, 85.37%, and 69.90%, respectively. CONCLUSIONS The multi-label learning method established using four diagnostic models based on mutual information feature selection yielded good recognition results. The characteristic model parameters were selected by maximizing the mutual information for each card type. The four diagnostic methods used to obtain information in CM, i.e., observation, auscultation and olfaction, inquiry, and pulse diagnosis, can be characterized by these parameters, which is consistent with CM theory.
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69
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Kang S, Doroshow R, McConnaughey J, Shekhar R. Automated Identification of Innocent Still's Murmur in Children. IEEE Trans Biomed Eng 2016; 64:1326-1334. [PMID: 27576242 DOI: 10.1109/tbme.2016.2603787] [Citation(s) in RCA: 17] [Impact Index Per Article: 2.1] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/09/2022]
Abstract
OBJECTIVE Still's murmur is the most common innocent heart murmur in children. It is also the most commonly misdiagnosed murmur, resulting in a high number of unnecessary referrals to pediatric cardiologist. The purpose of this study was to develop a computer algorithm for automated identification of Still's murmur that may help reduce unnecessary referrals. METHODS We first developed an accurate segmentation algorithm to locate the first and the second heart sounds. Once these sounds were identified, we extracted signal features specific to Still's murmur. Subsequently, machine learning-based classifiers, artificial neural network and support vector machine, were used to identify Still's murmur. RESULTS We evaluated our classifiers using the jackknife method using 87 Still's murmurs and 170 non-Still's murmurs. Our algorithm identified Still's murmur accurately with 84-93% sensitivity and 91-99% specificity. CONCLUSION We have achieved accurate automated identification of Still's murmur while minimizing false positives. The performance of our algorithm is comparable to the rate of murmur identification by auscultation by pediatric cardiologists. SIGNIFICANCE To our knowledge, our solution is the first murmur classifier that focuses singularly on Still's murmur. Following further refinement and testing, the presented algorithm could reduce the number of children with Still's murmur referred unnecessarily to pediatric cardiologists.
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70
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Moghaddasi H, Nourian S. Automatic assessment of mitral regurgitation severity based on extensive textural features on 2D echocardiography videos. Comput Biol Med 2016; 73:47-55. [DOI: 10.1016/j.compbiomed.2016.03.026] [Citation(s) in RCA: 35] [Impact Index Per Article: 4.4] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/27/2015] [Revised: 03/29/2016] [Accepted: 03/30/2016] [Indexed: 10/22/2022]
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71
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Malik S, Khadgawat R, Anand S, Gupta S. Non-invasive detection of fasting blood glucose level via electrochemical measurement of saliva. SPRINGERPLUS 2016; 5:701. [PMID: 27350930 PMCID: PMC4899397 DOI: 10.1186/s40064-016-2339-6] [Citation(s) in RCA: 34] [Impact Index Per Article: 4.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 01/12/2016] [Accepted: 05/11/2016] [Indexed: 01/02/2023]
Abstract
Machine learning techniques such as logistic regression (LR), support vector machine (SVM) and artificial neural network (ANN) were used to detect fasting blood glucose levels (FBGL) in a mixed population of healthy and diseased individuals in an Indian population. The occurrence of elevated FBGL was estimated in a non-invasive manner from the status of an individual’s salivary electrochemical parameters such as pH, redox potential, conductivity and concentration of sodium, potassium and calcium ions. The samples were obtained from 175 randomly selected volunteers comprising half healthy and half diabetic patients. The models were trained using 70 % of the total data, and tested upon the remaining set. For each algorithm, data points were cross-validated by randomly shuffling them three times prior to implementing the model. The performance of the machine learning technique was reported in terms of four statistically significant parameters—accuracy, precision, sensitivity and F1 score. SVM using RBF kernel showed the best performance for classifying high FBGLs with approximately 85 % accuracy, 84 % precision, 85 % sensitivity and 85 % F1 score. This study has been approved by the ethical committee of All India Institute of Medical Sciences, New Delhi, India with the reference number: IEC/NP-278/01-08-2014, RP-29/2014.
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Affiliation(s)
- Sarul Malik
- Center for Biomedical Engineering, Indian Institute of Technology (IIT) Delhi, New Delhi, 110016 Delhi India
| | - Rajesh Khadgawat
- Department of Endocrinology and Metabolism, All India Institute of Medical Sciences (AIIMS), New Delhi, 110016 Delhi India
| | - Sneh Anand
- Center for Biomedical Engineering, Indian Institute of Technology (IIT) Delhi, New Delhi, 110016 Delhi India ; Department of Biomedical Engineering, All India Institute of Medical Sciences (AIIMS), New Delhi, 110016 Delhi India
| | - Shalini Gupta
- Department of Chemical Engineering, Indian Institute of Technology (IIT) Delhi, New Delhi, 110016 Delhi India
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72
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Azar AT, Inbarani HH, Renuga Devi K. Improved dominance rough set-based classification system. Neural Comput Appl 2016. [DOI: 10.1007/s00521-016-2177-z] [Citation(s) in RCA: 12] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/22/2022]
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73
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Peng RC, Yan WR, Zhang NL, Lin WH, Zhou XL, Zhang YT. Cuffless and Continuous Blood Pressure Estimation from the Heart Sound Signals. SENSORS 2015; 15:23653-66. [PMID: 26393591 PMCID: PMC4610503 DOI: 10.3390/s150923653] [Citation(s) in RCA: 22] [Impact Index Per Article: 2.4] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 06/02/2015] [Revised: 09/09/2015] [Accepted: 09/09/2015] [Indexed: 11/26/2022]
Abstract
Cardiovascular disease, like hypertension, is one of the top killers of human life and early detection of cardiovascular disease is of great importance. However, traditional medical devices are often bulky and expensive, and unsuitable for home healthcare. In this paper, we proposed an easy and inexpensive technique to estimate continuous blood pressure from the heart sound signals acquired by the microphone of a smartphone. A cold-pressor experiment was performed in 32 healthy subjects, with a smartphone to acquire heart sound signals and with a commercial device to measure continuous blood pressure. The Fourier spectrum of the second heart sound and the blood pressure were regressed using a support vector machine, and the accuracy of the regression was evaluated using 10-fold cross-validation. Statistical analysis showed that the mean correlation coefficients between the predicted values from the regression model and the measured values from the commercial device were 0.707, 0.712, and 0.748 for systolic, diastolic, and mean blood pressure, respectively, and that the mean errors were less than 5 mmHg, with standard deviations less than 8 mmHg. These results suggest that this technique is of potential use for cuffless and continuous blood pressure monitoring and it has promising application in home healthcare services.
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Affiliation(s)
- Rong-Chao Peng
- Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences, Shenzhen 518055, China.
- Shenzhen College of Advanced Technology, University of Chinese Academy of Sciences, Shenzhen 518055, China.
- Key Lab for Health Informatics of Chinese Academy of Sciences (HICAS), Shenzhen 518055, China.
| | - Wen-Rong Yan
- Department of Physics and Materials Science, City University of Hong Kong, Hong Kong 999077, China.
| | - Ning-Ling Zhang
- Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences, Shenzhen 518055, China.
- Shenzhen College of Advanced Technology, University of Chinese Academy of Sciences, Shenzhen 518055, China.
- Key Lab for Health Informatics of Chinese Academy of Sciences (HICAS), Shenzhen 518055, China.
| | - Wan-Hua Lin
- Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences, Shenzhen 518055, China.
- Key Lab for Health Informatics of Chinese Academy of Sciences (HICAS), Shenzhen 518055, China.
| | - Xiao-Lin Zhou
- Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences, Shenzhen 518055, China.
- Key Lab for Health Informatics of Chinese Academy of Sciences (HICAS), Shenzhen 518055, China.
| | - Yuan-Ting Zhang
- Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences, Shenzhen 518055, China.
- Key Lab for Health Informatics of Chinese Academy of Sciences (HICAS), Shenzhen 518055, China.
- Department of Electronic Engineering, Chinese University of Hong Kong, Hong Kong 999077, China.
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74
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Leng S, Tan RS, Chai KTC, Wang C, Ghista D, Zhong L. The electronic stethoscope. Biomed Eng Online 2015; 14:66. [PMID: 26159433 PMCID: PMC4496820 DOI: 10.1186/s12938-015-0056-y] [Citation(s) in RCA: 138] [Impact Index Per Article: 15.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/20/2015] [Accepted: 06/11/2015] [Indexed: 11/13/2022] Open
Abstract
Most heart diseases are associated with and reflected by the sounds that the heart produces. Heart auscultation, defined as listening to the heart sound, has been a very important method for the early diagnosis of cardiac dysfunction. Traditional auscultation requires substantial clinical experience and good listening skills. The emergence of the electronic stethoscope has paved the way for a new field of computer-aided auscultation. This article provides an in-depth study of (1) the electronic stethoscope technology, and (2) the methodology for diagnosis of cardiac disorders based on computer-aided auscultation. The paper is based on a comprehensive review of (1) literature articles, (2) market (state-of-the-art) products, and (3) smartphone stethoscope apps. It covers in depth every key component of the computer-aided system with electronic stethoscope, from sensor design, front-end circuitry, denoising algorithm, heart sound segmentation, to the final machine learning techniques. Our intent is to provide an informative and illustrative presentation of the electronic stethoscope, which is valuable and beneficial to academics, researchers and engineers in the technical field, as well as to medical professionals to facilitate its use clinically. The paper provides the technological and medical basis for the development and commercialization of a real-time integrated heart sound detection, acquisition and quantification system.
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Affiliation(s)
- Shuang Leng
- National Heart Research Institute Singapore, National Heart Centre Singapore, 5 Hospital Drive, Singapore, 169609, Singapore.
| | - Ru San Tan
- National Heart Research Institute Singapore, National Heart Centre Singapore, 5 Hospital Drive, Singapore, 169609, Singapore.
- Cardiovascular and Metabolic Disorders Program, Duke-NUS Graduate Medical School, 8 College Road, Singapore, 169857, Singapore.
| | - Kevin Tshun Chuan Chai
- Institute of Microelectronics, A*STAR, 11 Science Park Road, Singapore, 117685, Singapore.
| | - Chao Wang
- Institute of Microelectronics, A*STAR, 11 Science Park Road, Singapore, 117685, Singapore.
| | | | - Liang Zhong
- National Heart Research Institute Singapore, National Heart Centre Singapore, 5 Hospital Drive, Singapore, 169609, Singapore.
- Cardiovascular and Metabolic Disorders Program, Duke-NUS Graduate Medical School, 8 College Road, Singapore, 169857, Singapore.
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75
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A novel method for discrimination between innocent and pathological heart murmurs. Med Eng Phys 2015; 37:674-82. [DOI: 10.1016/j.medengphy.2015.04.013] [Citation(s) in RCA: 27] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/11/2014] [Revised: 11/18/2014] [Accepted: 04/25/2015] [Indexed: 11/21/2022]
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76
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Sun S. An innovative intelligent system based on automatic diagnostic feature extraction for diagnosing heart diseases. Knowl Based Syst 2015. [DOI: 10.1016/j.knosys.2014.12.001] [Citation(s) in RCA: 14] [Impact Index Per Article: 1.6] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/24/2022]
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77
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Redlarski G, Gradolewski D, Palkowski A. A system for heart sounds classification. PLoS One 2014; 9:e112673. [PMID: 25393113 PMCID: PMC4231067 DOI: 10.1371/journal.pone.0112673] [Citation(s) in RCA: 44] [Impact Index Per Article: 4.4] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/29/2013] [Accepted: 08/28/2014] [Indexed: 12/03/2022] Open
Abstract
The future of quick and efficient disease diagnosis lays in the development of reliable non-invasive methods. As for the cardiac diseases – one of the major causes of death around the globe – a concept of an electronic stethoscope equipped with an automatic heart tone identification system appears to be the best solution. Thanks to the advancement in technology, the quality of phonocardiography signals is no longer an issue. However, appropriate algorithms for auto-diagnosis systems of heart diseases that could be capable of distinguishing most of known pathological states have not been yet developed. The main issue is non-stationary character of phonocardiography signals as well as a wide range of distinguishable pathological heart sounds. In this paper a new heart sound classification technique, which might find use in medical diagnostic systems, is presented. It is shown that by combining Linear Predictive Coding coefficients, used for future extraction, with a classifier built upon combining Support Vector Machine and Modified Cuckoo Search algorithm, an improvement in performance of the diagnostic system, in terms of accuracy, complexity and range of distinguishable heart sounds, can be made. The developed system achieved accuracy above 93% for all considered cases including simultaneous identification of twelve different heart sound classes. The respective system is compared with four different major classification methods, proving its reliability.
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Affiliation(s)
- Grzegorz Redlarski
- Department of Mechatronics and High Voltage Engineering, Gdansk University of Technology, Gdansk, Poland
| | - Dawid Gradolewski
- Department of Mechatronics and High Voltage Engineering, Gdansk University of Technology, Gdansk, Poland
| | - Aleksander Palkowski
- Department of Mechatronics and High Voltage Engineering, Gdansk University of Technology, Gdansk, Poland
- * E-mail:
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78
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Rochefort CM, Verma AD, Eguale T, Lee TC, Buckeridge DL. A novel method of adverse event detection can accurately identify venous thromboembolisms (VTEs) from narrative electronic health record data. J Am Med Inform Assoc 2014; 22:155-65. [PMID: 25332356 PMCID: PMC4433368 DOI: 10.1136/amiajnl-2014-002768] [Citation(s) in RCA: 52] [Impact Index Per Article: 5.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/04/2022] Open
Abstract
BACKGROUND Venous thromboembolisms (VTEs), which include deep vein thrombosis (DVT) and pulmonary embolism (PE), are associated with significant mortality, morbidity, and cost in hospitalized patients. To evaluate the success of preventive measures, accurate and efficient methods for monitoring VTE rates are needed. Therefore, we sought to determine the accuracy of statistical natural language processing (NLP) for identifying DVT and PE from electronic health record data. METHODS We randomly sampled 2000 narrative radiology reports from patients with a suspected DVT/PE in Montreal (Canada) between 2008 and 2012. We manually identified DVT/PE within each report, which served as our reference standard. Using a bag-of-words approach, we trained 10 alternative support vector machine (SVM) models predicting DVT, and 10 predicting PE. SVM training and testing was performed with nested 10-fold cross-validation, and the average accuracy of each model was measured and compared. RESULTS On manual review, 324 (16.2%) reports were DVT-positive and 154 (7.7%) were PE-positive. The best DVT model achieved an average sensitivity of 0.80 (95% CI 0.76 to 0.85), specificity of 0.98 (98% CI 0.97 to 0.99), positive predictive value (PPV) of 0.89 (95% CI 0.85 to 0.93), and an area under the curve (AUC) of 0.98 (95% CI 0.97 to 0.99). The best PE model achieved sensitivity of 0.79 (95% CI 0.73 to 0.85), specificity of 0.99 (95% CI 0.98 to 0.99), PPV of 0.84 (95% CI 0.75 to 0.92), and AUC of 0.99 (95% CI 0.98 to 1.00). CONCLUSIONS Statistical NLP can accurately identify VTE from narrative radiology reports.
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Affiliation(s)
- Christian M Rochefort
- Faculty of Medicine, Ingram School of Nursing, McGill University, Montreal, Canada McGill Clinical and Health Informatics Research Group, McGill University, Montreal, Canada Department of Epidemiology, Biostatics and Occupational Health, Faculty of Medicine, McGill University, Montreal, Canada
| | - Aman D Verma
- McGill Clinical and Health Informatics Research Group, McGill University, Montreal, Canada Department of Epidemiology, Biostatics and Occupational Health, Faculty of Medicine, McGill University, Montreal, Canada
| | - Tewodros Eguale
- McGill Clinical and Health Informatics Research Group, McGill University, Montreal, Canada Brigham and Women's Hospital, Boston, Massachusetts, USA
| | - Todd C Lee
- McGill University Health Centre (MUHC), Montreal, Canada
| | - David L Buckeridge
- McGill Clinical and Health Informatics Research Group, McGill University, Montreal, Canada Department of Epidemiology, Biostatics and Occupational Health, Faculty of Medicine, McGill University, Montreal, Canada
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79
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ZHANG WENYING, GUO XINGMING, YUAN ZHIHUI, ZHU XINGHUA. HEART SOUND CLASSIFICATION AND RECOGNITION BASED ON EEMD AND CORRELATION DIMENSION. J MECH MED BIOL 2014. [DOI: 10.1142/s0219519414500468] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/18/2022]
Abstract
Analysis of heart sound is of great importance to the diagnosis of heart diseases. Most of the feature extraction methods about heart sound have focused on linear time-variant or time-invariant models. While heart sound is a kind of highly nonstationary and nonlinear vibration signal, traditional methods cannot fully reveal its essential properties. In this paper, a novel feature extraction approach is proposed for heart sound classification and recognition. The ensemble empirical mode decomposition (EEMD) method is used to decompose the heart sound into a finite number of intrinsic mode functions (IMFs), and the correlation dimensions of the main IMF components (IMF1~IMF4) are calculated as feature set. Then the classical Binary Tree Support Vector Machine (BT-SVM) classifier is employed to classify the heart sounds which include the normal heart sounds (NHSs) and three kinds of abnormal signals namely mitral stenosis (MT), ventricular septal defect (VSD) and aortic stenosis (AS). Finally, the performance of the new feature set is compared with the correlation dimensions of original signals and the main IMF components obtained by the EMD method. The results showed that, for NHSs, the feature set proposed in this paper performed the best with recognition rate of 98.67%. For the abnormal signals, the best recognition rate of 91.67% was obtained. Therefore, the proposed feature set is more superior to two comparative feature sets, which has potential application in the diagnosis of cardiovascular diseases.
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Affiliation(s)
- WENYING ZHANG
- College of Bioengineering, Chongqing University, Key Laboratory of Biorheological Science and Technology, Ministry of Education, Chongqing 400044, P. R. China
| | - XINGMING GUO
- College of Bioengineering, Chongqing University, Key Laboratory of Biorheological Science and Technology, Ministry of Education, Chongqing 400044, P. R. China
| | - ZHIHUI YUAN
- College of Bioengineering, Chongqing University, Key Laboratory of Biorheological Science and Technology, Ministry of Education, Chongqing 400044, P. R. China
| | - XINGHUA ZHU
- College of Bioengineering, Chongqing University, Key Laboratory of Biorheological Science and Technology, Ministry of Education, Chongqing 400044, P. R. China
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80
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Sun S, Jiang Z, Wang H, Fang Y. Automatic moment segmentation and peak detection analysis of heart sound pattern via short-time modified Hilbert transform. COMPUTER METHODS AND PROGRAMS IN BIOMEDICINE 2014; 114:219-230. [PMID: 24657095 DOI: 10.1016/j.cmpb.2014.02.004] [Citation(s) in RCA: 29] [Impact Index Per Article: 2.9] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 09/02/2013] [Revised: 01/05/2014] [Accepted: 02/11/2014] [Indexed: 06/03/2023]
Abstract
This paper proposes a novel automatic method for the moment segmentation and peak detection analysis of heart sound (HS) pattern, with special attention to the characteristics of the envelopes of HS and considering the properties of the Hilbert transform (HT). The moment segmentation and peak location are accomplished in two steps. First, by applying the Viola integral waveform method in the time domain, the envelope (E(T)) of the HS signal is obtained with an emphasis on the first heart sound (S1) and the second heart sound (S2). Then, based on the characteristics of the E(T) and the properties of the HT of the convex and concave functions, a novel method, the short-time modified Hilbert transform (STMHT), is proposed to automatically locate the moment segmentation and peak points for the HS by the zero crossing points of the STMHT. A fast algorithm for calculating the STMHT of E(T) can be expressed by multiplying the E(T) by an equivalent window (W(E)). According to the range of heart beats and based on the numerical experiments and the important parameters of the STMHT, a moving window width of N=1s is validated for locating the moment segmentation and peak points for HS. The proposed moment segmentation and peak location procedure method is validated by sounds from Michigan HS database and sounds from clinical heart diseases, such as a ventricular septal defect (VSD), an aortic septal defect (ASD), Tetralogy of Fallot (TOF), rheumatic heart disease (RHD), and so on. As a result, for the sounds where S2 can be separated from S1, the average accuracies achieved for the peak of S1 (AP₁), the peak of S2 (AP₂), the moment segmentation points from S1 to S2 (AT₁₂) and the cardiac cycle (ACC) are 98.53%, 98.31% and 98.36% and 97.37%, respectively. For the sounds where S1 cannot be separated from S2, the average accuracies achieved for the peak of S1 and S2 (AP₁₂) and the cardiac cycle ACC are 100% and 96.69%.
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Affiliation(s)
- Shuping Sun
- Department of Mechanical Engineering, Yamaguchi University, Tokiwadai 2-16-1, Ube, Yamaguchi, Japan.
| | - Zhongwei Jiang
- Department of Mechanical Engineering, Yamaguchi University, Tokiwadai 2-16-1, Ube, Yamaguchi, Japan
| | - Haibin Wang
- School of Electrical and Information Engineering, Xihua University, Chengdu 610039, China
| | - Yu Fang
- School of Electrical and Information Engineering, Xihua University, Chengdu 610039, China
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Dong C, Wang Y, Zhang Q, Wang N. The methodology of Dynamic Uncertain Causality Graph for intelligent diagnosis of vertigo. COMPUTER METHODS AND PROGRAMS IN BIOMEDICINE 2013; 113:162-174. [PMID: 24176413 DOI: 10.1016/j.cmpb.2013.10.002] [Citation(s) in RCA: 13] [Impact Index Per Article: 1.2] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 02/13/2013] [Revised: 08/12/2013] [Accepted: 10/02/2013] [Indexed: 06/02/2023]
Abstract
Vertigo is a common complaint with many potential causes involving otology, neurology and general medicine, and it is fairly difficult to distinguish the vertiginous disorders from each other accurately even for experienced physicians. Based on comprehensive investigations to relevant characteristics of vertigo, we propose a diagnostic modeling and reasoning methodology using Dynamic Uncertain Causality Graph. The symptoms, signs, findings of examinations, medical histories, etiology and pathogenesis, and so on, are incorporated in the diagnostic model. A modularized modeling scheme is presented to reduce the difficulty in model construction, providing multiple perspectives and arbitrary granularity for disease causality representations. We resort to the "chaining" inference algorithm and weighted logic operation mechanism, which guarantee the exactness and efficiency of diagnostic reasoning under situations of incomplete and uncertain information. Moreover, the causal insights into underlying interactions among diseases and symptoms intuitively demonstrate the reasoning process in a graphical manner. These solutions make the conclusions and advices more explicable and convincing, further increasing the objectivity of clinical decision-making. Verification experiments and empirical evaluations are performed with clinical vertigo cases. The results reveal that, even with incomplete observations, this methodology achieves encouraging diagnostic accuracy and effectiveness. This study provides a promising assistance tool for physicians in diagnosis of vertigo.
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Affiliation(s)
- Chunling Dong
- School of Computer Science and Engineering, Beihang University, Beijing 100191, China; Shandong Normal University, Jinan 250014, China.
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Marascio G, Modesti PA. Current trends and perspectives for automated screening of cardiac murmurs. HEART ASIA 2013; 5:213-8. [PMID: 27326133 PMCID: PMC4832733 DOI: 10.1136/heartasia-2013-010392] [Citation(s) in RCA: 7] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/22/2013] [Accepted: 08/22/2013] [Indexed: 01/19/2023]
Abstract
Although in high income countries rheumatic heart disease is now rare, it remains a major burden in low and middle income countries. In these world areas, physicians and expert sonographers are rare, and screening campaigns are usually performed by nomadic caregivers who can only recognise patients in an advanced phase of heart failure with high economic and social costs. Therefore, great interest exists regarding the possibility of developing a simple, low-cost procedure for screening valvular heart disease. With the development of computer science, the cardiac sound signal can be analysed in an automatic way. More precisely, a panel of features characterising the acoustic signal are extracted and sent to a decision-making software able to provide the final diagnosis. Although no system is currently available in the market, the rapid evolution of these technologies recently led to the activation of clinical trials. The aim of this note is to review the state of advancement of this technology (trends in feature selection and automatic diagnostic strategies), data available regarding performance of the technology in the clinical setting and finally what obstacles still need to be overcome before automated systems can be clinically/commercially viable.
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Affiliation(s)
- Giuseppe Marascio
- Department of Clinical and Experimental Medicine, Clinica Medica Generale e Cardiologia, University of Florence, Florence, Italy
- Centre for Civil Protection and Risk Studies, University of Florence (CESPRO), Florence, Italy
| | - Pietro Amedeo Modesti
- Department of Clinical and Experimental Medicine, Clinica Medica Generale e Cardiologia, University of Florence, Florence, Italy
- Centre for Civil Protection and Risk Studies, University of Florence (CESPRO), Florence, Italy
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Talu MF, Gül M, Alpaslan N, Yiğitcan B. Calculation of melatonin and resveratrol effects on steatosis hepatis using soft computing methods. COMPUTER METHODS AND PROGRAMS IN BIOMEDICINE 2013; 111:498-506. [PMID: 23746907 DOI: 10.1016/j.cmpb.2013.04.020] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/04/2012] [Revised: 03/19/2013] [Accepted: 04/19/2013] [Indexed: 06/02/2023]
Abstract
In this work, beneficial effects of melatonin and resveratrol drugs on liver damage in rats, induced by application of acute and chronic carbon tetrachloride (CCl4) have been examined. The study consists of three main stages: (1) DATA ACQUISITION: light microscopic images were obtained from 60 rats separated into 10 groups after the preparation of liver tissue samples for histological examination. Rats in first five experimental groups for the four-day and the other five groups for twenty-day were examined. (2) Data processing: by the help of histograms of oriented gradient (HOG) method, obtaining low-dimensional image features (color, shape and texture) and classifying five different group characteristics by using these features with artificial neural networks (ANNs), and support vector machines (SVMs) have been provided. (3) Calculation of drug effectiveness: firstly to determine the differences between group characteristics of rats, a pilot group has been selected (diseased group-CCl4), and the responses of ANN and SVM trained by HOG features have been calculated. As a result of ANN, it has been seen that melatonin and resveratrol drugs have %65.62-%75.12 positive effects at the end of the fourth day, %84.12-%98.89 positive effects on healing steatosis hepatis at the end of the twentieth day respectively and as a result of SVM, it has been seen that melatonin and resveratrol drugs have %62.5-%68.75 positive effects at the end of the fourth day, %45.12-%60.89 positive effects on healing steatosis hepatis at the end of the twentieth day respectively.
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Affiliation(s)
- M Fatih Talu
- Inonu University, Department of Computer Engineering, Malatya, Turkey.
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84
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Chen CK, Bruce M, Tyler L, Brown C, Garrett A, Goggins S, Lewis-Polite B, Weriwoh ML, Juarez PD, Hood DB, Skelton T. Analysis of an environmental exposure health questionnaire in a metropolitan minority population utilizing logistic regression and Support Vector Machines. J Health Care Poor Underserved 2013; 24:153-71. [PMID: 23395953 PMCID: PMC4061745 DOI: 10.1353/hpu.2013.0046] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/11/2022]
Abstract
The goal of this study was to analyze a 54-item instrument for assessment of perception of exposure to environmental contaminants within the context of the built environment, or exposome. This exposome was defined in five domains to include 1) home and hobby, 2) school, 3) community, 4) occupation, and 5) exposure history. Interviews were conducted with child-bearing-age minority women at Metro Nashville General Hospital at Meharry Medical College. Data were analyzed utilizing DTReg software for Support Vector Machine (SVM) modeling followed by an SPSS package for a logistic regression model. The target (outcome) variable of interest was respondent's residence by ZIP code. The results demonstrate that the rank order of important variables with respect to SVM modeling versus traditional logistic regression models is almost identical. This is the first study documenting that SVM analysis has discriminate power for determination of higher-ordered spatial relationships on an environmental exposure history questionnaire.
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Affiliation(s)
- Chau-Kuang Chen
- Department of Institutional Research at Meharry Medical College, Nashville, TN 37208, USA
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85
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Ding J, Cao Y, Mpofu E, Shi Z. A hybrid support vector machine and fuzzy reasoning based fault diagnosis and rescue system for stable glutamate fermentation. Chem Eng Res Des 2012. [DOI: 10.1016/j.cherd.2012.01.004] [Citation(s) in RCA: 10] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/14/2022]
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86
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Uğuz H. A hybrid system based on information gain and principal component analysis for the classification of transcranial Doppler signals. COMPUTER METHODS AND PROGRAMS IN BIOMEDICINE 2012; 107:598-609. [PMID: 21524813 DOI: 10.1016/j.cmpb.2011.03.013] [Citation(s) in RCA: 7] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 09/27/2010] [Revised: 02/17/2011] [Accepted: 03/26/2011] [Indexed: 05/30/2023]
Abstract
A transcranial Doppler (TCD) is a non-invasive, easy to apply and reliable technique which is used in the diagnosis of various brain diseases by measuring the blood flow velocities in brain arteries. This study aimed to classify the TCD signals, and feature ranking (information gain - IG) and dimension reduction methods (principal component analysis - PCA) were used as a hybrid to improve the classification efficiency and accuracy. In this context, each feature within the feature space was ranked depending on its importance for the classification using the IG method. Thus, the less important features were ignored and the highly important features were selected. Then, the PCA method was applied to the highly important features for dimension reduction. As a result, a hybrid feature reduction between the selection of the highly important features and the application of the PCA method on the reduced features were achieved. To evaluate the effectiveness of the proposed method, experiments were conducted using a support vector machine (SVM) classifier on the TCD signals recorded from the temporal region of the brain of 82 patients, as well as 24 healthy people. The experimental results showed that using the IG and PCA methods as a hybrid improves the classification efficiency and accuracy compared with individual usage.
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Affiliation(s)
- Harun Uğuz
- Department of Computer Engineering, Selçuk University, Konya, Turkey.
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87
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Chen Y, Wang S, Shen CH, Choy FK. Matrix decomposition based feature extraction for murmur classification. Med Eng Phys 2012; 34:756-61. [DOI: 10.1016/j.medengphy.2011.09.020] [Citation(s) in RCA: 27] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/19/2010] [Revised: 09/20/2011] [Accepted: 09/22/2011] [Indexed: 11/27/2022]
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88
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Adaptive neuro-fuzzy inference system for diagnosis of the heart valve diseases using wavelet transform with entropy. Neural Comput Appl 2011. [DOI: 10.1007/s00521-011-0610-x] [Citation(s) in RCA: 10] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/18/2022]
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89
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Son YJ, Kim HG, Kim EH, Choi S, Lee SK. Application of support vector machine for prediction of medication adherence in heart failure patients. Healthc Inform Res 2010; 16:253-9. [PMID: 21818444 PMCID: PMC3092139 DOI: 10.4258/hir.2010.16.4.253] [Citation(s) in RCA: 61] [Impact Index Per Article: 4.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/19/2010] [Accepted: 09/14/2010] [Indexed: 12/16/2022] Open
Abstract
Objectives Heart failure (HF) is a progressive syndrome that marks the end-stage of heart diseases, and it has a high mortality rate and significant cost burden. In particular, non-adherence of medication in HF patients may result in serious consequences such as hospital readmission and death. This study aims to identify predictors of medication adherence in HF patients. In this work, we applied a Support Vector Machine (SVM), a machine-learning method useful for data classification. Methods Data about medication adherence were collected from patients at a university hospital through self-reported questionnaire. The data included 11 variables of 76 patients with HF. Mathematical simulations were conducted in order to develop a SVM model for the identification of variables that would best predict medication adherence. To evaluate the robustness of the estimates made with the SVM models, leave-one-out cross-validation (LOOCV) was conducted on the data set. Results The two models that best classified medication adherence in the HF patients were: one with five predictors (gender, daily frequency of medication, medication knowledge, New York Heart Association [NYHA] functional class, spouse) and the other with seven predictors (age, education, monthly income, ejection fraction, Mini-Mental Status Examination-Korean [MMSE-K], medication knowledge, NYHA functional class). The highest detection accuracy was 77.63%. Conclusions SVM modeling is a promising classification approach for predicting medication adherence in HF patients. This predictive model helps stratify the patients so that evidence-based decisions can be made and patients managed appropriately. Further, this approach should be further explored in other complex diseases using other common variables.
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Affiliation(s)
- Youn-Jung Son
- Department of Nursing, Soonchunhyang University, Cheonan, Korea
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90
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Yu W, Liu T, Valdez R, Gwinn M, Khoury MJ. Application of support vector machine modeling for prediction of common diseases: the case of diabetes and pre-diabetes. BMC Med Inform Decis Mak 2010; 10:16. [PMID: 20307319 PMCID: PMC2850872 DOI: 10.1186/1472-6947-10-16] [Citation(s) in RCA: 158] [Impact Index Per Article: 11.3] [Reference Citation Analysis] [Abstract] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/18/2009] [Accepted: 03/22/2010] [Indexed: 12/04/2022] Open
Abstract
Background We present a potentially useful alternative approach based on support vector machine (SVM) techniques to classify persons with and without common diseases. We illustrate the method to detect persons with diabetes and pre-diabetes in a cross-sectional representative sample of the U.S. population. Methods We used data from the 1999-2004 National Health and Nutrition Examination Survey (NHANES) to develop and validate SVM models for two classification schemes: Classification Scheme I (diagnosed or undiagnosed diabetes vs. pre-diabetes or no diabetes) and Classification Scheme II (undiagnosed diabetes or pre-diabetes vs. no diabetes). The SVM models were used to select sets of variables that would yield the best classification of individuals into these diabetes categories. Results For Classification Scheme I, the set of diabetes-related variables with the best classification performance included family history, age, race and ethnicity, weight, height, waist circumference, body mass index (BMI), and hypertension. For Classification Scheme II, two additional variables--sex and physical activity--were included. The discriminative abilities of the SVM models for Classification Schemes I and II, according to the area under the receiver operating characteristic (ROC) curve, were 83.5% and 73.2%, respectively. The web-based tool-Diabetes Classifier was developed to demonstrate a user-friendly application that allows for individual or group assessment with a configurable, user-defined threshold. Conclusions Support vector machine modeling is a promising classification approach for detecting persons with common diseases such as diabetes and pre-diabetes in the population. This approach should be further explored in other complex diseases using common variables.
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Affiliation(s)
- Wei Yu
- National Office of Public Health Genomics, Coordinating Center for Health Promotion, Centers for Disease Control and Prevention, Atlanta, GA, USA.
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